Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (5): 1664-1672.doi: 10.13229/j.cnki.jdxbgxb20200467

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Optimization of urban rail transit operation adjustment based on multiple strategies under delay

Zuo-an HU1,2(),Yi-ming XIA1,Jia CAI1,Feng XUE1,2()   

  1. 1.School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 611756,China
    2.National Engineering Laboratory for Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu 611756,China
  • Received:2020-06-28 Online:2021-09-01 Published:2021-09-16
  • Contact: Feng XUE E-mail:huzuoan@swjtu.edu.cn;xuefeng.7@swjtu.edu.cn

Abstract:

In order to develop a more efficient train operation adjustment scheme in terms of recovering train delay and easing of passenger detention, an integrated train operation adjustment method was put forward, which includes a time-exceeding strategy, a train deduction strategy, a skip-stop strategy and a dynamic dwell time. Then, combined with the actual operating conditions of the train, the operation constraints and adjustment constraints were set up, and a model with the target of minimizing the total travel time was established. Finally, according to the characteristics of the model, a corresponding nested genetic algorithm was designed to solve the model. The results of the case show that compared with the conventional train operation adjustment method, the proposed integrated adjustment method can make the train which was directly affected by delay complete the delay recovery in advance in various kinds of situations, and can effectively alleviate the long waiting time or detention of passengers caused by the delay.

Key words: transportation planning and management, train delay, operation adjustment, nested genetic algorithm

CLC Number: 

  • U231.92

Fig.1

Diagram of train operation adjustment"

Table 1

Parameters definitions"

参数定义
N受延误影响列车集合,N={1,?,i,?,n}
M线路车站集合,M={1,?,k,?,j,?,m}
s,m0初始延误列车及初始延误发生站点,sN,m0M
ts,tg初始延误发生时刻及调度人员对初始延误持续时间做出预估所需时间
N1初始延误列车前行列车集合,N1={1,2,?,s-1}
N2

初始延误列车及其后行列车集合,

N2={s,s+1,?,n}

ai,j,di,j列车ij站的实际到达及出发时刻
Ai,j,Di,j列车ij站的计划到达及出发时刻
Rj,Rj'列车在jj+1站之间的最小及计划区间运行时间
I0,I1,I2列车计划追踪间隔、最小发到间隔及最小追踪间隔
wi,j,Wi,j列车ij站的实际和计划停站时间
Δwi,j列车ij站的停站时间增加量
Pi,j列车ij站出发时车内人数
Bi,j,Ei,j列车ij站的实际上车及下车人数
BkOD,EkODOD表中在k站的总上车及下车人数
Ek,jODOD表中在k站上车并在j站下车的人数
qi,j列车ij站面临的上车需求人数
li,j列车ij站出发时,仍在j站滞留的人数
δjj站的乘客到达速率,人/s
θj站站停模式下j站乘客下车比例
ξi,j列车ij站的下车比例调整因数
Cmax,DO列车最大载客人数及一侧车门数
τ1,τ2列车起动及停车附加时间
DE最大总扣车时间
λ1,λ2出行终到站被跳停的乘客选择在出行终到站的前一站及后一站下车的比例
xi,j列车ij站跳停决策的0-1变量,xi,j=1为列车ij站跳停,xi,j=0则为未跳停

Fig.2

Solution process of nested genetic algorithm"

Fig.3

Structure of outer and inner chromosomes"

Fig.4

OD during morning peak hours"

Fig.5

Convergence of function"

Fig.6

Train diagram under two adjustment schemes"

Table 2

Increase in dwell time of forward trains"

列车车站
S4S5S6S7S8S9S10S11S12S13S19
T9---120090000
T10-850160107800
T112300000265700

Table 3

Comparison of results"

调整方案乘客总旅行时间/h站台乘客候车时间/h乘客乘车时间/h
计算结果与计划值之差优化效果计算结果与计划值之差优化效果计算结果与计划值之差优化效果
计划运行方案19 605--2 097--17 508--
常规调整方案20 9971 392-4 3052 208-16 692-816-
一体化调整方案20 17957458.8%3 8151 71822.2%16 364-1 14440.2%

Table 4

Adjustment scheme and optimization effect in three situations"

延误情形初始延误列车影响列车集合扣车方案跳停方案优化效果/%
乘客总旅行时间站台乘客候车时间乘客乘车时间
高峰+一般延误T12N={1,2,?,21}T10在S5扣车11 s,T11在S4扣车19 s均未跳停42.233.5-10.8
平峰+较长延误T7N={1,2,?,10}T5在S7扣车8 s,T6在S5扣车70 sT7跳停S3及S666.233.221.1
平峰+一般延误T7N={1,2,?,8}均未扣车均未跳停57.426.31400
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